106 citations · 214 across the 14 of their papers we have counts for
14 papers
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
Peter Samoaa, Linus Aronsson, Antonio Longa +2
Most machine learning and data analytics applications, including performance engineering in software systems, require a large number of annotations and labelled data, which might n…
Using Microbenchmark Suites to Detect Application Performance Changes
Martin Grambow, Denis Kovalev, Christoph Laaber +2
Software performance changes are costly and often hard to detect pre-release. Similar to software testing frameworks, either application benchmarks or microbenchmarks can be integr…
Machine Learning Systems are Bloated and Vulnerable
Huaifeng Zhang, Fahmi Abdulqadir Ahmed, Dyako Fatih +4
Today's software is bloated with both code and features that are not used by most users. This bloat is prevalent across the entire software stack, from operating systems and applic…
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks
Hazem Peter Samoaa, Antonio Longa, Mazen Mohamad +2
Predicting the performance of production code prior to actually executing or benchmarking it is known to be highly challenging. In this paper, we propose a predictive model, dubbed…
Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications
Joel Scheuner, Simon Eismann, Sacheendra Talluri +4
Making serverless computing widely applicable requires detailed performance understanding. Although contemporary benchmarking approaches exist, they report only coarse results, do…
Facing the Giant: a Grounded Theory Study of Decision-Making in Microservices Migrations
Hamdy Michael Ayas, Philipp Leitner, Regina Hebig
Background: Microservices migrations are challenging and expensive projects with many decisions that need to be made in a multitude of dimensions. Existing research tends to focus…